What is Starwhale

Starwhale is an MLOps platform. It provides Instance, Project, Runtime, Model, and Dataset.

• Instance: Each installation of Starwhale is called an instance.

  • 👻 Standalone Instance: The simplest form that requires only the Starwhale Client(swcli). swcli is written by pure python3.
  • 🎍 On-Premises Instance: Cloud form, we call it private cloud instance. Kubernetes and BareMetal both meet the basic environmental requirements.
  • ☁️ Cloud Hosted Instance: Cloud form, we call it public cloud instance. Starwhale team maintains the web service.

  Starwhale tries to keep concepts consistent across different types of instances. In this way, people can easily exchange data and migrate between them.

• Project: The basic unit for organizing different resources.

• ML Basic Elements: The Machine Learning/Deep Learning running environments or artifacts. Starwhale empowers the ML/DL essential elements with packaging, versioning, reproducibility, and shareability.

  • 🐌 Runtime: Software dependencies description to "run" a model, which includes python libraries, native libraries, native binaries, etc.
  • 🐇 Model: The standard model format used in model delivery.
  • 🐫 Dataset: A unified description of how the data and labels are stored and organized. Starwhale datasets can be loaded efficiently.

• Running Fundamentals: Starwhale uses Job, Step, and Task to execute ML/DL actions like model training， evaluation, and serving. Starwhale's Controller-Agents structure scales out easily.

  • 🥕 Job: A set of programs to do specific work. Each job consists of one or more steps.
  • 🌵 Step: Represents distinct stages of the work. Each step consists of one or more tasks.
  • 🥑 Task: Operation entity. Tasks are in some specific steps.

• Scenarios: Starwhale provides the best practice and out-of-the-box for different ML/DL scenarios.

  • 🚝 Model Training(TBD): Use Starwhale Python SDK to record experiment meta, metric, log, and artifact.
  • 🛥️ Model Evaluation: PipelineHandler and some report decorators can give you complete, helpful, and user-friendly evaluation reports with only a few lines of codes.
  • 🛫 Model Serving(TBD): Starwhale Model can be deployed as a web service or stream service in production with deployment capability, observability, and scalability. Data scientists do not need to write ML/DL irrelevant codes.

MNIST Quick Tour for the standalone instance

• 🍰 STEP1: Installing Starwhale

  python3 -m pip install --pre starwhale

• 🍵 STEP2: Downloading the MNIST example

  git clone https://github.com/star-whale/starwhale.git
  cd starwhale/example/mnist

• ☕ STEP3: Building a runtime

  swcli runtime create -n pytorch-mnist -m venv --python 3.9 .
  source venv/bin/activate
  python3 -m pip install -r requirements.txt
  swcli runtime build .
  swcli runtime info pytorch-mnist/version/latest

• 🍞 STEP4: Building a model

  • Write some code with Starwhale Python SDK. Complete code is here.

  from starwhale.api.model import PipelineHandler
  from starwhale.api.metric import multi_classification
  
  class MNISTInference(PipelineHandler):
  
      def __init__(self):
          super().__init__(merge_label=True, ignore_error=False)
          self.model = self._load_model()
  
      def ppl(self, data:bytes, batch_size:int, **kw):
          data = self._pre(data, batch_size)
          output = self.model(data)
          return self._post(output)
  
      def handle_label(self, label:bytes, batch_size:int, **kw):
          return [int(l) for l in label]
  
      @multi_classification(
          confusion_matrix_normalize="all",
          show_hamming_loss=True,
          show_cohen_kappa_score=True,
          show_roc_auc=True,
          all_labels=[i for i in range(0, 10)],
      )
      def cmp(self, _data_loader:"DataLoader"):
          _result, _label, _pr = [], [], []
          for _data in _data_loader:
              _label.extend([int(l) for l in _data[self._label_field]])
              # unpack data according to the return value of function ppl
              (pred, pr) = _data[self._ppl_data_field]
              _result.extend([int(l) for l in pred])
              _pr.extend([l for l in pr])
  
      def _pre(self, input:bytes, batch_size:int):
          """write some mnist preprocessing code"""
  
      def _post(self, input:bytes):
          """write some mnist post-processing code"""
  
      def _load_model():
          """load your pre trained model"""

  • Define model.yaml.

  name: mnist
  model:
    - models/mnist_cnn.pt
  config:
    - config/hyperparam.json
  run:
    ppl: mnist.ppl:MNISTInference

  • Run one command to build the model.

   swcli model build .
   swcli model info mnist/version/latest

• 🍺 STEP5: Building a dataset

  • Download MNIST RAW data files.

   mkdir -p data && cd data
   wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
   wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
   gzip -d *.gz
   cd ..
   ls -lah data/*

  • Write some code with Starwhale Python SDK. Full code is here.

   from starwhale.api.dataset import BuildExecutor
  
   class DataSetProcessExecutor(BuildExecutor):
  
       def iter_data_slice(self, path: str):
           """read data file, unpack binary data, yield data bytes"""
  
       def iter_label_slice(self, path: str):
           """read label file, unpack binary data, yield label bytes"""

  • Define dataset.yaml.

   name: mnist
   mode: generate
   data_dir: data
   data_filter: "t10k-image*"
   label_filter: "t10k-label*"
   process: mnist.process:DataSetProcessExecutor
   attr:
     batch_size: 50
     alignment_size: 4k
     volume_size: 2M

  • Run one command to build the dataset.

   swcli dataset build .
   swcli dataset info mnist/version/latest

• 🍖 STEP6: Running an evaluation job

   swcli -vvv job create --model mnist/version/latest --dataset mnist/version/latest
   swcli job list
   swcli job info ${version}

👏 Now, you have completed the fundamental steps for Starwhale standalone.

Let's go ahead and finish the tutorial on the on-premises instance.

MNIST Quick Tour for on-premises instance

🍰 STEP1: installing minikube and helm

• Minikube 1.25+
• Helm 3.2.0+

There are standard installation tutorials for minikube and helm here.

🍵 STEP2: Start minikube

minikube start
alias kubectl="minikube kubectl --"

For users in the mainland of China, please add these startup parameters：--image-mirror-country='cn' --image-repository=registry.cn-hangzhou.aliyuncs.com/google_containers for minikube start

Installation process

helm repo add starwhale https://star-whale.github.io/charts
helm repo update
helm install my-starwhale starwhale/starwhale --version 0.2.0 -n starwhale --create-namespace --set minikube.enabled=true

After the installation is successful, the following prompt message appears:

NAME: my-starwhale
LAST DEPLOYED: Thu Jun 23 14:48:02 2022
NAMESPACE: starwhale
STATUS: deployed
REVISION: 1
NOTES:
******************************************
Chart Name: starwhale
Chart Version: 0.2.0
App Version: 0.2.0
...

Port Forward Visist:
  - starwhale controller:
    - run: kubectl port-forward --namespace starwhale svc/my-starwhale-controller 8082:8082
    - visit: http://localhost:8082
  - minio admin:
    - run: kubectl port-forward --namespace starwhale svc/my-starwhale-minio 9001:9001
    - visit: http://localhost:9001
  - mysql:
    - run: kubectl port-forward --namespace starwhale svc/my-starwhale-mysql 3306:3306
    - visit: mysql -h 127.0.0.1 -P 3306 -ustarwhale -pstarwhale

******************************************
Login Info:
- starwhale: u:starwhale, p:abcd1234
- minio admin: u:minioadmin, p:minioadmin

*_* Enjoy using Starwhale. *_*

Then keep checking the minikube service status until all pods are running.

watch -n 1 kubectl get pods -n starwhale

NAME	READY	STATUS	RESTARTS	AGE
my-starwhale-agent-cpu-64699	2/2	Running	0	1m
my-starwhale-controller-7d864558bc-vxvb8	1/1	Running	0	1m
my-starwhale-minio-7d45db75f6-7wq9b	1/1	Running	0	2m
my-starwhale-mysql-0	1/1	Running	0	2m

Make the Starwhale controller accessible locally with the following command:

kubectl port-forward --namespace starwhale svc/my-starwhale-controller 8082:8082

☕ STEP3: Upload the artifacts to the cloud instance

pre-prepared artifacts Before starting this tutorial, the following three artifacts should already exist on your machine：

• a starwhale model named mnist
• a starwhale dataset named mnist
• a starwhale runtime named pytorch-mnist

The above three artifacts are what we built on our machine using starwhale.

1. Use swcli to operate the remote server First, log in to the server:

   swcli instance login --username starwhale --password abcd1234 --alias server http://localhost:8082

   Use the server instance as the default:

   swcli instance select server

2. Create a project named 'project_for_mnist' and make it default:

   swcli project create project_for_mnist
   swcli project select project_for_mnist

3. Start copying the model, dataset, and runtime that we constructed earlier:

   swcli model copy local/project/self/model/mnist/version/latest http://localhost:8082/
   swcli dataset copy local/project/self/dataset/mnist/version/latest http://localhost:8082/
   swcli runtime copy local/project/self/runtime/pytorch-mnist/version/latest http://localhost:8082/

🍞 STEP4: Use the web UI to run an evaluation

1. login Ok, let's use the username(starwhale) and password(abcd1234) to open the server web UI(http://localhost:8082/).

2. Then, we will see the project named 'project_for_mnist' that we created earlier with swcli. Click the project name, you will see the model, runtime, and dataset uploaded in the previous step.

3. Create an evaluation job

4. The job is completed and the results can be viewed

Congratulations! You have completed the evaluation process for a model.

Documentation, Community, and Support

• Visit Starwhale HomePage.

• More information in the official documentation.

• For general questions and support, join the Slack.

• For bug reports and feature requests, please use Github Issue.

• To get community updates, follow @starwhaleai on Twitter.

• For Starwhale artifacts, please visit:

  • Python Package on Pypi.
  • Helm Charts on Artifacthub.
  • Docker Images on Docker Hub and ghcr.io.

• Additionally, you can always find us at [email protected].

Contributing

🌼👏PRs are always welcomed 👍🍺.

License

Starwhale is licensed under the Apache License 2.0.